Multi-Level Contextual Network for Biomedical Image Segmentation
This work addresses the problem of accurate and reliable segmentation for biomedical image analysis, offering an incremental improvement with a novel method for known bottlenecks in contextual integration.
The paper tackles biomedical image segmentation by proposing a deep fully convolutional residual network with a new skip connection strategy to integrate local and global contextual patterns, resulting in a computationally inexpensive model that provides fast and effective pixel-wise dense predictions on epithelium and tubule segmentation tasks.
Accurate and reliable image segmentation is an essential part of biomedical image analysis. In this paper, we consider the problem of biomedical image segmentation using deep convolutional neural networks. We propose a new end-to-end network architecture that effectively integrates local and global contextual patterns of histologic primitives to obtain a more reliable segmentation result. Specifically, we introduce a deep fully convolution residual network with a new skip connection strategy to control the contextual information passed forward. Moreover, our trained model is also computationally inexpensive due to its small number of network parameters. We evaluate our method on two public datasets for epithelium segmentation and tubule segmentation tasks. Our experimental results show that the proposed method provides a fast and effective way of producing a pixel-wise dense prediction of biomedical images.